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Since 1987 - Covering the Fastest Computers in the World and the People Who Run ThemFri, 09 Dec 2016 21:51:05 +0000en-UShourly1https://wordpress.org/?v=4.760365857Enlisting Deep Learning in the War on Cancerhttps://www.hpcwire.com/2016/12/07/enlisting-deep-learning-war-cancer/?utm_source=rss&utm_medium=rss&utm_campaign=enlisting-deep-learning-war-cancer
https://www.hpcwire.com/2016/12/07/enlisting-deep-learning-war-cancer/#respondWed, 07 Dec 2016 16:08:30 +0000https://www.hpcwire.com/?p=32475Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. The pilots, supported in part by DOE exascale funding, not only seek to do good by advancing cancer research and therapy but also to advance deep learning capabilities and infrastructure with an eye towards eventual use on exascale machines.

]]>Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. The pilots, supported in part by DOE exascale funding, not only seek to do good by advancing cancer research and therapy but also to advance deep learning capabilities and infrastructure with an eye towards eventual use on exascale machines.

By any standard, the U.S. War on Cancer and the Precision Medicine Initiative’s (PMI) are ambitious. Past Wars on Cancer haven’t necessarily fared well, which is not to say much hasn’t been accomplished. Today’s timing seems more promising. Progress in biomedical science and the ramp-up of next gen leadership computers (en route to exascale) are powerful enablers. Stir in the rapid emergence of deep learning to exploit data-driven science and many see greater cause for optimism. Not by chance was the opening plenary panel at SC16 on precision medicine and the role of HPC.

The three JDACS4C pilots span molecular to population scale efforts in support of the CANcer Distributed Learning Environment (CANDLE) project: they are intended to “provide insight into scalable machine learning tools; deep learning, simulation and analytics to reduce time to solution; and inform design of future computing solutions.” The hope is also to establish “a new paradigm for cancer research for years to come by making effective use of the ever-growing volumes and diversity of cancer-related data to build predictive models, provide better understanding of the disease and, ultimately, provide guidance and support decisions on anticipated outcomes of treatment for individual patients.”

Rick Stevens, ANL

These are ambitious goals. Sorting out JDACS4C’s precise lineage is a little challenging – it falls broadly under the Precision Medicine Initiative, NCI Cancer Moonshot, and has been also lumped under NSCI. Stevens noted the early discussion to create the effort started a couple of years ago with the first funding issued in the August time frame. Here’s a snapshot of the three pilots:

RAS Molecular Project. This project (Molecular Level Pilot for RAS Structure and Dynamics in Cellular Membranes) is intended to develop new computational approaches supporting research already being done under the RAS Initiative. Ultimately the hope is to refine our understanding of the role of the RAS (gene family) and its associated signaling pathway in cancer and to identify new therapeutic targets uniquely present in RAS protein membrane signaling complexes.

Pre-Clinical Screening. This project (Cellular Level Pilot for Predictive Modeling for Pre-clinical Screening) will develop “machine learning, large-scale data and predictive models based on experimental biological data derived from patient-derived xenografts.” The idea is to create a feedback loop, where the experimental models inform the design of the computational models. These predictive models may point to new targets in cancer and help identify new treatments.

As the projects came together, “We realized each had a need for deep learning and different uses of it. So the idea is that we would all work together on building both the software environment and network topologies and everything we would need for the three projects so we wouldn’t have duplication,” said Stevens. The researchers defined key benchmarks that “are tractable kinds of deep learning problems that are aligned with what we have to solve for the different cancer sub problems.”

An early first step was attracting vendor participation – something that turned out to be easy said Stevens because virtually all the major HPC vendors are aggressively ramping up DL roadmaps. Most see the JDACS4C pilots as opportunities to learn and refine. Stevens said JDASC4C has collaborations with Intel, Cray, NVIDIA, IBM, among others.

“All of the labs have DGX-1s and NVIDIA has optimized most of the common frameworks for the different GPUs, Pascal, etc. The DGX-1 is like an appliance so anything we build that runs on the DGX-1 can be easily distributed. Intel has it own extensive plans and not all is public yet. I can say that we are collaborating with all the right parts of Intel,” said Stevens, an ANL researcher and leader of the pre-clinical screening project.

Indeed Intel has been busy, buying Nervana (a complete platform for DL) and recently rolling out expanded plans. “They are talking about versions of Knights X series that are optimized for machine learning. Knights Mill is the first version of that part of their roadmap,” said Stevens. The chip giant also introduced a DL inference accelerator card at SC16; it’s a field-programmable gate array (FPGA)-based hardware and software solution for neural network acceleration. Stevens suggests Intel, like NVIDIA, is developing an appliance strategy.

“Intel is very much trying to define a strategy that differentiates some level between the platform for training and for inferencing. Most deep learning systems now do inferencing on the ‘quasi’ client side – on smaller platforms than used for training. Intel wants to ensure “future IA architectures are good at inferencing,” he said.

Not surprisingly there’s a fair amount of effort assessing the many DL frameworks coming out of the Google, Microsoft, Facebook et al. “We are evaluating which frameworks work best for our problems and we are working with vendors to optimize them on the hardware. We’re also working with Livermore which has an internal project to build a scalable artificial neural network framework call LBANN,” said Stevens.

The plan is to develop “our models in a way that is independent of the frameworks so we can swap out the frameworks as those evolve without having to recode our models. This is a very common approach with deep learning where you have a scripting layer that captures your model representation – the meta algorithms for training and management data, etc. – and we are working with both the academic community and the NVIDIA on the workflow engine at the top. So we have kind of a stacked architecture and it involves collaborating with all of the different groups around the DL landscape.”

“What’s interesting,” said Stevens, “is the vendors for the next-gen platforms are strongly embracing the architectural ideas and features needed for accelerated machine learning in addition to traditional kind of physics-driven simulation.” He noted that market pressures and the fast growth of DL compared to the traditional HPC are pushing them in this direction. “It’s also giving us insight into DOE applications that are going to start looking like this – where there will be traditional physics-driven simulation, but where often we can find a way to leverage machine learning [too].”

Sharing the learning is an important component of the pilot projects. “We are abstracting model problems for the machine learning community to work on that are kind of sanitized versions of the seven candle benchmarks we’re working on,” said Stevens. That will include distributable data, code, all to be available at GitHub. The first of those elements are expected in Q2.

Individual pilot teams are also mounting their own outreach activities with the academic community. In terms of compute power for the pilots, “We are targeting platforms, particularly the CORAL platforms, new machines at Argonne, Oak Ridge and Livermore, and [eventually] exascale. Everything is sort of ecumenical so its not GPU specific or manycore specific.”

It’s interesting to look at the different ways in which the three projects plan to use deep learning.

The RAS project, at the molecular scale, is the smallest dimensional scale of all of the projects. RAS, you may know, is a well-known family of oncogenes that code for signaling proteins embedded in the cell membrane. These proteins control signaling pathways that extend into the cell and drive very many diverse cellular processes. RAS is currently implicated in about 30 percent of cancers including some of the toughest such as pancreatic cancer. The pilot project will combine simulation and wet lab screening data in an effort to elaborate the details of the RAS-related signaling cascades and hopefully identify key places to intervene and new drugs to use.

Even a relatively small tumor may have “thousands of mutations, both driver mutations and many passenger mutations,” said Stevens. These genetic miscues can alter the important details of signaling networks. For many years RAS itself as well as its associated signaling networks have been drug targets but as Stevens pointed out, “the behavior of that signaling network is very non-intuitive. Sometimes if you hit one of the downstream components, it actually creates negative feedback, which actually increases the effect you are trying to inhibit.”

In the RAS project, the simulation is basically a molecular dynamics exercise conducted at various granularities extending all the way down to atomistic behavior including quantum effects. The computational power required, not surprisingly, depends on the level of granularity being simulated and can be substantial.

“Machine learning is being used to track the state space that the simulation is going through and to make decisions – do we zoom in here, do we zoom out, do we change the parameters that we are looking in a different part to the ensemble space. It’s basically acting like a smart supervisor of this simulation to more effectively use it.

“In some sense it’s like the network is watching a movie and saying, “OK, I’ve seen this part of the movie before, let’s fast forward, or wow this is really interesting I’ve never seen this before, let’s use slow motion and zoom in.” That’s sort of what the machine learning is doing in the simulation. It’s able to fast forward and skip around in some sense,” said Stevens.

The pre-clinical screening project, led by Stevens, is an ambitious effort to sift through basically as much cancer preclinical and clinical data as it can lay hold of and combine that with new data generated from mouse models to build predictive models of drug-tumor interactions. It’s an in silico and experimental feedback approach. Ultimately, given a specific tumor whose molecular attributes (gene expression, SNPs, proteomics, etc) have been characterized, it should be possible to plug that data into a model to determine the best therapeutic regime.

The subtlety here, said Stevens, is there has been a lot of machine learning work in this done at kind of the small scale, that is on single classes of tumors or relatively small classes of drugs. “What we are trying to do with the deep learning is to integrate all of this information across thousands of cell lines, tens of thousands of compounds that have been screened against smaller number of cell lines, and then be able to project that into a mouse. You grow a colony of mice derived from that human tumor, and these mice become proxies for human clinical trials. So I can try different compounds on the colony of tumor mice to provide information about how my tumor might respond to them if given as a drug.”

A huge challenge is being able to make sense of all the historical data, much of which is unstructured and often subjective (e.g. pathology reports). “One of the first things that we have done is to build classifiers to tell us what type the tumor is or where the body site is [based on diverse data],” he said. Not infrequently the data may be suspect. “If it’s a new dataset we run it through our classifiers and they may say, really, this is not from the liver, it’s from some other place.”

As a rule, the preclinical data is outcome based; it doesn’t explain how the result was achieved.

“Right now we can build machine learning models that are pretty accurate at say predicting a drug response or tumor type or outcome but they can’t tell us very effectively why. They are not explanatory, not mechanistic,” said Stevens, “What we want to do is bring in mechanistic models or mechanistic data in some way and hybridize that with machine learning models so that we get two things. We get the ability to have a highly accurate predictable model but also a model that can explain why that prediction. So the idea of this hybrid approach is a wide open space and we think that this will generalize into many fields.” Obtaining large and high quality data for training models remains challenging, he said.

The third project strives to develop models able to make population scale forecasts, what Stevens call “patient trajectories.” It’s basically mining surveillance data across the country. Although somewhat dispersed, there is a great deal of patient data held by NCI, NIH, FDA, pharma, and payor organizations (pathology reports, treatments, outcomes, lifestyle, demographics, etc.). Unfortunately, like a lot of biomedical data, it’s largely unstructured. “We can’t really compute on it in the way we want to so we are using machine learning to translate the unstructured data into structured data we can compute on,” said Stevens

“So for example we want to read all the pathology reports with a machine and pull out, say the biomarkers, the mutational state, or the drugs and so on such that we can then build profiles that are consistent. Think of it as a population-based model. In preclinical screening pilot let’s say we uncover some treatments and strategies that are very effective on a certain type of cancer. We want to take that information and feed it into the population model and say “If this became a common therapy, how much would it change the statistics globally or nationally” or something like that.”

It’s also a way to link all of the pilots, said Steven. Insight from the RAS project might be later used to look at subclasses of cancers where the new treatment might work; that in turn put it into the population model to understand what the impact of that might be.

It’s still early days for the JDACS4C pilot projects, but hopes are high. Stevens noted both NCI and DOE are getting access to things they don’t readily have. “NCI does not have a lot of mathematicians and computer scientists, which DOE has. They also don’t have access to leadership machines. What we (DOE) are getting is access to all of this great experimental data, experimental facilities, public databases.”

]]>https://www.hpcwire.com/2016/12/07/enlisting-deep-learning-war-cancer/feed/032475Ganthier, Turkel on the Dell EMC Road Aheadhttps://www.hpcwire.com/2016/12/05/ganthier-turkel-dell-emc-road-ahead/?utm_source=rss&utm_medium=rss&utm_campaign=ganthier-turkel-dell-emc-road-ahead
https://www.hpcwire.com/2016/12/05/ganthier-turkel-dell-emc-road-ahead/#respondMon, 05 Dec 2016 22:33:23 +0000https://www.hpcwire.com/?p=32406Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off.

]]>Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off.

Indeed, contends Ganthier, if you combine Dell and EMC revenue (server and storage), the entity overtakes HPE as the top dog in the market. Doubtless HPE would say otherwise. No matter. In this SC16 video interview Ganthier and Ed Turkel, senior strategist, Dell EMC, talk with HPCwire editor John Russell about Dell EMC’s synergies, ambitions to both democratize HPC and advance it at the high end, and strategies for moving forward.

A major element of the Dell EMC strategy is a solutions focus that incorporates technology from both sides of the new company. A good example, says Turkel, is a genomics solution demo’d at SC16 that features Dell compute and incorporates Isilon’s (EMC) new all flash offering. Isilon is already widely used in life sciences research HPC.

Jim Ganthier, Dell EMC

Moreover, says Ganthier, Dell EMC is continuing Dell’s recent efforts to make it easier to acquire and use ‘validated solutions’ for various domain areas. One tool is Dell EMC’s systems builder, an automated web-based ‘configurator’. First steps are answering business outcome and technology workload questions. It’s aimed at SMB and smaller enterprises.

“Take life sciences for example,” says Ganthier. “Questions like how many genomes do you plan to look at? Are you going to keep them on premise or off premise? What kinds of software are you using? The system will not only give you a configuration but a quote, that is modifiable, but if you like it as is, you can order it.” Machine learning, financial services, and manufacturing are also verticals targeted.

Dell EMC also plans to play at the high end. It won the contract for TACC’s Stampede 2.0 – which may make its way into the top ten when installed. More details of Stampede 2.0 are likely around the ISC17 timeframe, says Ganthier. There’s more in the video interview providing a good summary of Dell EMC’s early HPC plans.

]]>https://www.hpcwire.com/2016/12/05/ganthier-turkel-dell-emc-road-ahead/feed/032406HPE-SGI to Tackle Exascale and Enterprise Targetshttps://www.hpcwire.com/2016/11/22/hpe-sgi-tackle-exascale-enterprise-targets/?utm_source=rss&utm_medium=rss&utm_campaign=hpe-sgi-tackle-exascale-enterprise-targets
https://www.hpcwire.com/2016/11/22/hpe-sgi-tackle-exascale-enterprise-targets/#respondTue, 22 Nov 2016 23:19:55 +0000https://www.hpcwire.com/?p=32146At first blush, and maybe second blush too, Hewlett Packard Enterprise’s (HPE) purchase of SGI seems like an unambiguous win-win. SGI’s advanced shared memory technology, its popular UV product line (Hanna), deep vertical market expertise, and services-led go-to-market capability all give HPE a leg up in its drive to remake itself. Bear in mind HPE came into existence just a year ago with the split of Hewlett-Packard. The computer landscape, including HPC, is shifting with still unclear consequences. One wonders who’s next on the deal block following Dell’s recent merger with EMC.

]]>At first blush, and maybe second blush too, Hewlett Packard Enterprise’s (HPE) purchase of SGI seems like an unambiguous win-win. SGI’s advanced shared memory technology, its popular UV product line (HANA), deep vertical market expertise, and services-led go-to-market capability all give HPE a leg up in its drive to remake itself. Bear in mind HPE came into existence just a year ago with the split of Hewlett-Packard. The computer landscape, including HPC, is shifting with still unclear consequences. One wonders who’s next on the deal block following Dell’s recent merger with EMC.

Few details of HPE plans for SGI (product roadmap, etc.) are available. At SC16 HPE was busy getting at least one message out – it is delighted for the new resources and high-end HPC headroom provided by SGI. “It’s a strategic move on our part to bolster our position in the HPC market as well as in the big data and mission critical systems spaces. [SGI has] lots of technologies across high performance data analytics (HPDA) and real time transaction processing in addition to HPC especially at the very high-end,” said Vineeth Ram, VP, HPC, Big Data & IoT Segment, HPE Servers, during a briefing at SC.

There’s still plenty to sort out. One issue that may prove troublesome is SGI’s preexisting reseller arrangements with Dell EMC, now a direct competitor, and Cisco, for the SGI’s UV Hanna machines. Some staff rightsizing may also be necessary. Yet on paper what’s most eye-catching is the deal’s complementary rather than overlapping quality.

Vineeth Ram, HPE

More details will be provided in the opening half of 2017, said Ram. Job one, perhaps predictably, is connecting the two sales teams and reassuring customers, said Ram, “If you think about product roadmap and [what] our strategy is, we will continue investing in all the capabilities and are committed to supporting our existing offerings across our portfolios. We want to make sure customers investments are protected, we want to make sure we take that into account as we define the roadmaps.”

The deal itself (see HPCwire article, HPE Gobbles SGI for Larger Slice of $11B HPC Pie) was for about $275M and ends the seven-year reprieve that kept the SGI banner flying after Rackable Systems purchased the bankrupt Silicon Graphics Inc. for $25 million in 2009 and assumed the SGI brand. There was plenty to covet for HPE. Ram noted, “HPE had consciously made our strategy more of a mid-end approach and then start making a foray into the higher end. SGI’s history has been very much at the high end.”

When the acquisition formally closed three weeks ago, SGI became part of HPE’s infrastructure group, headed by Antonio Neri, executive vice president and general manager. It’s noteworthy that Bill Mannel, a long-time SGI exec, moved to HP roughly a year ahead of the HP split-up, and is now vice president and GM of High-Performance Computing and Big Data for HPE. He should know the SGI organization and portfolio as well as anyone and will likely play a role in guiding incorporation of SGI into the HPE fold.

In its annual HPC market update at SC, IDC still pegged HPE as the market share leader in HPC servers by a nearly 2x advantage over Dell EMC (see IDC chart taken from its SC16 Update and read HPCwire article, IDC: AI, HPDA Driving HPC into High Growth Markets, for more); Dell EMC disputes that leadership assertion suggesting that now its combined revenues with EMC push it to top. Bragging rights aside, HPE was chasing more than a revenue bump and new products, said Ram. SGI’s go-to-market strategy, sales channels, and vertical segment (industry and geography) expertise were at least as important.

Here are four (of many) important benefits SGI brings according to Ram:

Verticals. SGI is particularly strong in life sciences, government, climate simulation, and manufacturing, for example, which all represent HPE opportunities. “Even in spaces like manufacturing, where we have been playing, I think we have been playing at the slightly lower end and SGI has come in at the higher end,” said Ram. SGI has built up tremendous domain expertise in these areas as well, he said.

Services. A fair amount of SGI business is created by its services and integration capabilities – a departure from HPE’s somewhat more product-led strategy. Ram noted SGI’s extensive end-to-end approach spans everything from the hardware to software stacks and applications. It’s not the HPE doesn’t have these capabilities, he said, but it is true SGI leverages and leads with them.

Japan. Cracking the Japanese market is historically difficult. Reliance on Japanese manufacturers is high. Contrarily, SGI is one of few U.S.-based computer companies that have fared well there, to a significant degree leveraging deep systems integration capabilities there, said Ram.

U.S. Manufacturing. This is no doubt an important element of SGI’s strength in selling to the U.S. government, and perhaps likely to become more important under the new Administration and if the National Strategic Computing Initiative gathers steam.

Like virtually all HPC server suppliers, HPE has been singing the ‘purpose-built’ chorus and the SGI acquisition doesn’t change that at all. It does elevate the target landscape HPE can attack. “They have domain expertise, deep vertical expertise, and solutions folks who can actually bring their super computer capability and drive it across the enterprise,” said RAM.

One of the more interesting aspects to the acquisitions to watch will be HPE’s highest-end strategy. HP/HPE has for some time touted its leadership on the Top500 in terms of number of machines placed; that said, none of the top ten, for example, are HP/HPE systems. Mannel in an earlier interview and Ram at SC both emphasized HPE’s determination to penetrate the top end more effectively.

SGI technology should help this aspiration. For example an SGI ICE-X machine at energy giant Total was eleventh on the June 2016 Top500 (~5 Pflops) and dropped to 16 on the most recent list. Another SGI machine (SGI ICE XA, Cheyenne at the National Center for Atmospheric Research was 20th on the most recent list and a third SGI machine was 27th.

Ram calls the addition of SGI expertise exciting and said HPE was “absolutely” planning to play in the big sandbox including competing in big government procurement cycles leading towards exascale.

“I can say we are going to engage in bigger projects and longer term development with some of the uniqueness that we are bringing together. You will see us make some public statements early next year to talk about some of the kinds of exascale capabilities that we are driving, so we are definitely committed to investing and being part of the whole exascale evolution,” said Ram, hinting that HPE was already engaged in government projects but couldn’t able to elaborate until later in 2017.

]]>https://www.hpcwire.com/2016/11/22/hpe-sgi-tackle-exascale-enterprise-targets/feed/032146SDSC Announces New Life Sciences Computing Initiativehttps://www.hpcwire.com/off-the-wire/sdsc-announces-new-life-sciences-computing-initiative/?utm_source=rss&utm_medium=rss&utm_campaign=sdsc-announces-new-life-sciences-computing-initiative
https://www.hpcwire.com/off-the-wire/sdsc-announces-new-life-sciences-computing-initiative/#respondFri, 18 Nov 2016 14:39:36 +0000https://www.hpcwire.com/?post_type=off-the-wire&p=32042Nov. 18 — Spurred by the increasing reliance of life sciences researchers in the academic and private sectors on computational methods and data-enabled science, the San Diego Supercomputer Center (SDSC) at the University of California San Diego has inaugurated a new life sciences computing initiative focused on improving the performance of bioinformatics applications and associated […]

]]>Nov. 18 — Spurred by the increasing reliance of life sciences researchers in the academic and private sectors on computational methods and data-enabled science, the San Diego Supercomputer Center (SDSC) at the University of California San Diego has inaugurated a new life sciences computing initiative focused on improving the performance of bioinformatics applications and associated analysis pipelines on current and future advanced computing systems.

Dramatic improvements in scientific instruments and techniques, such as Next Generation Sequencing (NGS) and Cryo-electron Microscopy (Cryo-EM), are enabling the rapid accumulation of vast amounts of data including DNA/RNA molecular sequences and high-resolution imaging of biological structures from all manner of animal and plant organisms. The research and biotech community faces daunting challenges to manage and analyze this trove of data.

“Our experience with both on-campus researchers and biotech companies is that refined bioinformatics techniques and new technologies can be leveraged to improve the breadth and throughput of analyses,” noted Wayne Pfeiffer, an SDSC Distinguished Scientist and the Center’s bioinformatics lead.

Researchers are employing computational methods and analytics to derive scientific insights and commercial innovations from these growing pools of data, while advances in high-performance computing, storage, and networking are required to keep pace and help enable new discoveries. SDSC’s initiative will focus on developing and applying rigorous approaches to assessing and characterizing computational methods and pipelines. It will then specify the architectures, platforms, and technologies for optimizing along dimensions such as performance and throughput.

“UC San Diego is a pillar in one of the most vibrant life science research ecosystems in the world, and life science computing has always been a major focus of SDSC,” said SDSC Director Michael Norman. “Over the past decade we have seen unprecedented growth in the use of advanced computing by researchers in this area, and we view this partnership as a unique opportunity to bring the combined experience of SDSC, Dell EMC, and Intel to bear on one of the most important areas of data-intensive research today.”

The initial work, supported by Dell EMC and Intel, focuses on benchmarking and profiling selected genomic and Cryo-EM analysis pipelines and developing targeted recommendations for technical architectures to service those pipelines. Architectural recommendations will encompass integrated computing and storage platforms as well as networking fabrics.

“Dell EMC is dedicated to enabling and advancing the state-of-the-art for HPC solutions from the workgroup to the Top500,” said Jim Ganthier, senior vice president, Validated Solutions and HPC Organization, Dell EMC. “We are pleased to collaborate with both Intel and SDSC to help drive dramatic performance improvements and results in life sciences research.”

For more information about SDSC and opportunities for other organizations to get involved, please contact Ron Hawkins, Director of Industrial Relations.

About SDSC

As an Organized Research Unit of UC San Diego, SDSC is considered a leader in data-intensive computing and cyberinfrastructure, providing resources, services, and expertise to the national research community, including industry and academia. Cyberinfrastructure refers to an accessible, integrated network of computer-based resources and expertise, focused on accelerating scientific inquiry and discovery. SDSC supports hundreds of multidisciplinary programs spanning a wide variety of domains, from earth sciences and biology to astrophysics, bioinformatics, and health IT. SDSC’s Comet joins the Center’s data-intensive Gordon cluster, and are both part of the National Science Foundation’s XSEDE (Extreme Science and Engineering Discovery Environment) program.

]]>https://www.hpcwire.com/off-the-wire/sdsc-announces-new-life-sciences-computing-initiative/feed/032042SC16 Precision Medicine Panel Proves HPC Mattershttps://www.hpcwire.com/2016/11/16/sc16-precision-medicine-panel-proves-hpc-matters/?utm_source=rss&utm_medium=rss&utm_campaign=sc16-precision-medicine-panel-proves-hpc-matters
https://www.hpcwire.com/2016/11/16/sc16-precision-medicine-panel-proves-hpc-matters/#respondWed, 16 Nov 2016 20:15:03 +0000https://www.hpcwire.com/?p=31991In virtually every way, precision medicine (PM) is a poster child for the HPC Matters mantra and was a good choice for the Monday panel opening SC16 (HPC Impacts on Precision Medicine: Life’s Future – The Next Frontier in Healthcare). PM’s tantalizing promise is to touch all of us, not just writ large but individually – effectively fighting disease, enhancing health and lifestyle, extending life, and necessarily contributing to basic science along the way. All of this can only done with HPC.

]]>In virtually every way, precision medicine (PM) is the poster child for the HPC Matters mantra and was a good choice for the Monday panel opening SC16 (HPC Impacts on Precision Medicine: Life’s Future–The Next Frontier in Healthcare). PM’s tantalizing promise is to touch all of us, not just writ large but individually – effectively fighting disease, enhancing health and lifestyle, extending life, and necessarily contributing to basic science along the way. All of this can only happen with HPC.

Moderated by Steve Conway of IDC, five distinguished panelists from varying disciplines painted a powerful picture of PM’s prospects and challenges. Rather than dwell down-in-the-weeds on HPC technology minutiae, the panel tackled the broad sweep of data-driven science, mixed workload infrastructure, close collaboration across domains and organizations, and the need to make use of incremental advances while still pursuing transformational change.

It was a conversation with wide scope and difficult to summarize. Here are the panelists and a sound bite from their opening comments:

Mitchell Cohen, director of surgery, Denver Health Medical Center; Professor, University of Colorado School of Medicine. “If you get shot, stabbed, or run over I am your guy – a good person not to need,” quipped Cohen, momentarily underplaying his equal strength in basic medical research.

Warren Kibbe, director, Center for Biomedical Informatics and Information Technology (CBIIT); CIO, acting deputy director, National Cancer Institute. “[ACS] estimates there will be 1.7M new cases of cancer in the U.S. along and 14M worldwide this year. Six hundred thousand will die. [However] the mortality rate in cancer has been declining year since about 2000 so we are doing something right but it’s clear we need to understand more about basic biology,” said Kibbe.

Steve Scott, chief technology officer, Cray Inc. “I’m the computer guy. We tend to talk about Pflops [and the like]. The real disconnect is between the computational science world and clinical scientist and physicians. We need build solutions those people can use,” said Scott who dove a bit deeper into the simulation and analytics technologies and the computer architecture required to deliver PM.

Fred Streitz, LLNL

Fred Streitz, chief computational scientist and director of the High Performance Computing Innovation Center at Lawrence Livermore Lab.[i] Talking about a population scale data collection pilot that’s part of the CANcer Distributed Learning Environment (CANDLE), said Streitz: “[It’s] where rubber hits the roads. It’s focused on [establishing] an effective national cancer surveillance program that takes advantage of all of the data we currently have and are already collecting in different ways and states – [and will first use] natural language processing to makes sense of the data and regularize the data, and, then use machine learning to extract the information in a useful way.”

Martha Head, senior director, The Noldor; acting head, Insights from Data at GlaxoSmithKline Pharmaceuticals. She tackled the lengthy and problematic drug R&D cycle (a decade) from hypothesis to therapy. “We have to go faster [and not] with the same processes and just rushing ever faster. We need transformation, a new approach that combines simulation HPC and data analytics with experiment – a new engineering paradigm that almost treats an experiment as a subroutine or a function in a larger algorithm that we are running in our drug discovery process,” said Head.

Setting the stage, Conway emphasized zeroing in on the most appropriate care and preventative treatment is also financially imperative. The U.S. spent about $3 Trillion on healthcare in 2014 and is headed to $4.8 Trillion in 2021. Other countries do a bit better, with healthcare spending claiming 9-11 percent of GDP, yet that too is alarming.

PM, he said, will not only help save lives but also curb costs. It’s also becoming an important HPC market, so much that IDC is tracking dozens of healthcare initiatives around the world and will add PM as a new market segment it tracks within commercial analytics. Clearly the stakes are high.

Warren Kibbe, NCI

Kibbe, a key player in NCI’s Moonshot program, is a powerful advocate of HPC tools’ capacity to advance medicine through database creation, machine-learning based techniques, and a variety of simulation. That said, he cautioned, the biggest hurdle remains unknown biology. We simply do not know enough basic biology. This is a point echoed by a few others. Basic research as part of PM overall will help.

The Cancer Moonshot, he noted, has been carefully road-mapping what it thinks can be impactful and done. CANDLE is one of those efforts. He noted a blue ribbon NCI panel has spelled out clear objectives in a publically available report. Here are a few of its directional findings:

These and other efforts, driven by HPC, will work over time. One example is creation of the NCI Genomic Data Commons intended to provide the cancer research community with a unified data repository that enables data sharing across cancer genomic studies in support of precision medicine. “I want to give a shout out to Bob Grossman and his team at the University of Chicago,” said Kibbe of the project. The idea is “to help take data out of existing repositories and get it into the cloud so people can use cloud computing more effectively.”

Kibbe offered a realistically measured view of the Cancer Moonshot’s goal. It will make significant, meaningful progress, but it’s a long road towards whatever it is that actually constitutes a cure for al cancers. Head of GSK agreed and emphasized the value of public-private collaborations like the one GSK has with NCI.

As described by NCI, “Department of Energy, NCI, and GlaxoSmithKline are forming a new public–private partnership designed to harness high-performance computing and diverse biological data to accelerate the drug discovery process and bring new cancer therapies from target to first in human trials in less than a year. This partnership will bring together scientists from multiple disciplines to advance our understanding of cancer by finding patterns in vast and complex datasets to accelerate the development of new cancer therapies.”

Given the wealth of genomics data and the relative paucity of mechanistic information, pattern recognition and database analysis have been primary tools in pursuing PM. Recent advancement in these data-driven science techniques and their increasing use on HPC infrastructure are well aligned with PM purposes said Scott. The emerging HPC system model, which emphasizes memory and data movement as well as intense computation (lots of flops) is a good fit for PM.

“Computational demands and algorithm complexity are pushing us to build larger and larger machines, like Cori at NERSC, but they are fortunately pushing us in the direction of broader HPC. Computations tends to get all of the attention, [but] the real way to build a SC today depends upon the memory systems and interconnect,” said Scott. A mixed workload environment is what’s needed and also where supercomputing is trending.

“On the software side common HPC techniques like simulation done on molecular dynamics or finite element analysis or image processing can be brought to bear fairly successfully on PM problems [while similarly] areas like large scale graph analytics and machine learning are also critical.”

Streitz reviewed directions of CANDLE’s three pilot projects (see figure below) one of which seeks to unravel the role of RAS mutations, current in about 30 percent of cancer including some of the toughest, zeroing in how RAS behaves on the cell membrane. RAS is involved in growth and when it gets stuck in the on position, cancer can be the result.

Just today, it was announced that NVIDIA will join the project. Here’s an excerpt from the release:

“AI will be essential to achieve the objectives of the Cancer Moonshot,” said Rick Stevens, associate laboratory director for Computing, Environment and Life Sciences at Argonne National Laboratory. “New computing architectures have accelerated the training of neural networks by 50 times in just three years, and we expect more dramatic gains ahead.”

“GPU deep learning has given us a new tool to tackle grand challenges that have, up to now, been too complex for even the most powerful supercomputers,” said Jen-Hsun Huang, founder and chief executive officer, NVIDIA.

One of the most interesting observations came from Cohen. To some extent PM is trying to capture the knowledge experienced clinicians already have and codify it and make it available. Think of the time required to train a complicate neural network, matching answers to desire outcomes based on experience, as akin to clinical training and experience. Some clinicians still push back against this idea, calling it autonomous medicine that will claim or erode their jobs said Cohen.

This was clearly not his view. It’s also less about how PM can contribute to progress and more about its implementation. Still it suggested creating physician friendly tools and changing physician attitudes is at least a part of the challenge.

Capturing the full scope of the SC16 panel is a tall order. PM is a broad undertaking with many components. The NCI Cancer Moonshot is making progress daily, as demonstrated by today’s NVIDIA announcement. Precision medicine, which depends critically on HPC, matters.

[i] Streitz fiilled for Dimitri Kusnezov, Chief Scientist & Senior Advisor to the Secretary, U.S. Department of Energy, National Nuclear Security Administration, who was stuck in San Francisco because of travel problems.

]]>SALT LAKE CITY, Utah, Nov. 14 — HPCwire, the leading publication for news and information for the high performance computing industry announced the winners of the 2016 HPCwire Readers’ and Editors’ Choice Awards at the Supercomputing Conference (SC16) taking place this week in Salt Lake City, UT. Tom Tabor, CEO of Tabor Communications Inc., unveiled the list of winners just before the opening gala reception.

“From thought leaders to end users, the HPCwire readership reaches and engages every corner of the high performance computing community,” said Tom Tabor, CEO of Tabor Communications, publisher of HPCwire. “Receiving their recognition signifies community support across the entire HPC space as well as the breadth of industries it serves. Each year we honored to engage with our readership through the Readers’ and Editors’ choice program, and to recognize these efforts and make the voices of our readers heard. Our thanks and highest congratulations go out to all the winners for their outstanding breakthroughs and achievements.”

HPCwire has designated two categories of Awards: (1) Readers’ Choice, where winners have been determined through election by HPCwire readers, and (2) Editors’ Choice, where winners have been selected by a panel of HPCwire editors and thought leaders in HPC. The process started with an open nomination process, with voting taking place throughout the month of September. These awards are widely recognized as being among the most prestigious recognition given by the HPC community to its own each year.

The 2016 HPCwire Readers’ and Editors’ Choice Award winners are:

Best Use of HPC Application in Life Sciences

Readers’ Choice: Seagate collaborates to provide Hudson Alpha with a high performance, high density storage solution to manage large amounts of data generated from Illumina next-gen sequencers.

Editors’ Choice (TIE): The San Diego Supercomputer Center (SDSC) and Mellanox have collaborated to create a virtual off-loading computing processor used by SDSC Comet to optimize the processing of partner workloads and Nimbix pioneered the use of computational accelerators, including GPUs, FPGAs, DSPs and Intel Xeon Phis in cloud computing and has enabled the adoption of cloud for HPC workloads.

Readers’ Choice: LANL and Seagate’s Cooperative Research and Development Agreement (CRADA) develops power-managed disk and software solutions for deep data archiving and other next gen technologies.

Editors’ Choice: PNNL’s Center for Advanced Technology Evaluation (CENATE), a program for early evaluation of technologies, is currently assessing products from Micron Technology, Penguin Computing, NVIDIA, IBM, Data Vortex and Mellanox.

Best HPC Collaboration Between Academia & Industry

Readers’ Choice (TIE): OpenPOWER Academic Discussion Group is a community within the OpenPOWER Foundation focused on collaboration between industry and academia to develop a broad ecosystem for the POWER architecture and The Texas Advanced Computing Center (TACC) Lonestar 5 supercomputer, now in full production and built off technologies from Cray, Intel & DataDirect Networks, anchors TACC’s collaboration with industry program.

Editors’ Choice: The Centre for High Performance Computing, part of South Africa’s Council for Scientific and Industrial Research, supports cutting-edge research with high impact on the South African economy.

HPCwire is the #1 news and information resource covering the fastest computers in the world and the people who run them. With a legacy dating back to 1986, HPCwire has enjoyed a history of world-class editorial and journalism, making it the news source of choice selected by science, technology and business professionals interested in high performance and data-intensive computing. Visit HPCwire at www.hpcwire.com

About Tabor Communications Inc.

Tabor Communications Inc. (TCI) is a media and services company dedicated to high-end, performance computing. As publisher of a complete advanced scale computing portfolio that includes HPCwire, Datanami, Enterprise Tech, and HPCwire Japan, TCI is the market-leader in online journalism covering emerging technologies within the high-tech industry, and a services company providing events, audience insights, and other services for companies engaged in performance computing in enterprise, government, and research. More information can be found at www.taborcommunications.com.

]]>https://www.hpcwire.com/off-the-wire/hpcwire-reveals-winners-2016-readers-editors-choice-awards-sc16-conference-salt-lake-city/feed/031736Bright Computing and Dell EMC to Demonstrate Integrated HPC Solutions at SC16https://www.hpcwire.com/off-the-wire/bright-computing-dell-emc-demonstrate-integrated-hpc-solutions-sc16/?utm_source=rss&utm_medium=rss&utm_campaign=bright-computing-dell-emc-demonstrate-integrated-hpc-solutions-sc16
https://www.hpcwire.com/off-the-wire/bright-computing-dell-emc-demonstrate-integrated-hpc-solutions-sc16/#respondMon, 14 Nov 2016 12:53:05 +0000https://www.hpcwire.com/?post_type=off-the-wire&p=31762Nov. 14 — Bright Computing, the leading provider of hardware-agnostic cluster and cloud management software, today announced that both Bright and Dell EMC will demonstrate their integrated solutions for HPC in a variety of disciplines including life sciences, manufacturing, and research computing at Supercomputing in Salt Lake City, November 14 – 17, 2016. Dell EMC has selected Bright Cluster Manager to provide infrastructure management technology for its HPC Systems, […]

]]>Nov. 14 — Bright Computing, the leading provider of hardware-agnostic cluster and cloud management software, today announced that both Bright and Dell EMC will demonstrate their integrated solutions for HPC in a variety of disciplines including life sciences, manufacturing, and research computing at Supercomputing in Salt Lake City, November 14 – 17, 2016.

Dell EMC has selected Bright Cluster Manager to provide infrastructure management technology for its HPC Systems, which enable small and medium-sized enterprises to accelerate their science, engineering and analytics. Dell EMC HPC Systems are future-ready, flexible solutions that are pre-tested and pre-validated to ensure that they deliver the right research and business outcomes. The Dell EMC Validated Solutions portfolio speeds deployment and simplifies operations and management of solutions for virtualization, OpenStack cloud, big data and analytics, high-performance computing and more, incorporating best-in-class building blocks from Dell EMC and its partners. Bright technology is included in the Dell EMC HPC System for Life Sciences, the Dell EMC HPC System for Manufacturing, and the Dell EMC System for Research.

Dell EMC and Bright’s integrated solutions offer several key benefits to end users:

Bright helps empower Dell EMC customers to allocate compute resources dynamically, across their HPC, big data and OpenStack environments as required, as well as bursting into the cloud

Bright is intuitive to learn and easily installs out of the box

Bright easily absorbs the complexity and ensures scalability of an HPC cluster

Bright Computing has a long track record working with Dell EMC, and 2016 sees the two companies’ missions align to speed customer time to value, insight and discovery. Bright and Dell EMC have collaborated to provide high value integrated HPC solutions to hundreds of customers in life sciences, manufacturing, and research computing, including CHPC, CSIRO, Virginia Bioinformatics, TGEN, Florida Atlantic University, Exeter University in the UK, and many more.

Rick Hill, VP Channel Sales at Bright Computing, commented; “Together, Bright and Dell EMC have created best of breed end user solutions that are contributing to breakthrough research and increasing time to value for our end users.”

“Dell EMC’s expertise, technology and partnerships deliver state-of-the-art HPC systems from the workgroup to the TOP500,” said Jim Ganthier, senior vice president, Validated Solutions and HPC Organization, Dell EMC. “With Bright Cluster Manager for HPC, our customers can quickly and easily install fully integrated systems and both monitor and manage them across their lifecycles, allowing our customers to focus on innovation as opposed to operations.”

Bright Computing is exhibiting at booth 3417 at SC16. Dell is exhibiting at booth 1217. To book a meeting with Bright, please email pr-team@brightcomputing.com.

]]>https://www.hpcwire.com/off-the-wire/bright-computing-dell-emc-demonstrate-integrated-hpc-solutions-sc16/feed/031762IDC’s Conway Sets Stage for SC16 Precision Medicine Panelhttps://www.hpcwire.com/2016/11/04/idc-conway-sc16-precision-medicine-panel/?utm_source=rss&utm_medium=rss&utm_campaign=idc-conway-sc16-precision-medicine-panel
https://www.hpcwire.com/2016/11/04/idc-conway-sc16-precision-medicine-panel/#respondFri, 04 Nov 2016 13:56:50 +0000https://www.hpcwire.com/?p=31373Kicking off SC this year is what promises to be a fascinating panel – HPC Impacts on Precision Medicine: Life’s Future–The Next Frontier in Healthcare. In this pre-SC16 Q&A, Steve Conway, research VP in IDC's High Performance Computing group and moderator of the panel, sets the stage. HPC, of course, has been transforming life sciences and medicine for nearly two decades.

]]>HPCwire:The fact that Precision Medicine is the opening panel at SC strongly suggests the growing importance of HPC in making PM and basic life science research possible. Recognizing SC is primarily a technology conference, could you frame the goals of this panel?

Steve Conway: Precision medicine, also called personalized medicine, promises to transform medical practice and healthcare spending by enabling called personalized diagnoses and treatment plans that are custom-tuned for each patient’s physiology, symptoms, medical history, DNA and even lifestyle. What constitutes a good outcome for a broken hand may be different for an office worker and a concert violinist. HPC is already playing a key role in early precision medicine initiatives around the world, by speeding up genome sequencing and by making it possible to quickly sift through millions of archived patient records to identify treatments that have had the best success rates for patients closely resembling the patient under investigation. Biology is fast becoming a digital science and healthcare analytics is one of the fastest-growing new market segments for HPC. Precision medicine is happening at the intersection of biology, medical practice, healthcare economics, and data science. The expert panel at SC’16 will explore this emerging domain from these varied perspectives, with special emphasis on the major role HPC has already started to play.

Warren Kibbe, NCI

This is a pretty august group:

Mitchell Cohen, Director of Surgery, Denver Health Medical Center; Professor, University of Colorado School of Medicine.

HPCwire: Today much what constitutes PM is big data analytics. Within this context: a) what are the key technologies (compute/architectures, storage, informatics, etc) being used, b) what are the big technology challenges/bottlenecks, and c) where do you expect near-term progress?

Conway: We’ll hear more about this from the experts on the panel, but in general the computer technologies being used today to support precision medicine vary from purpose-built supercomputers such as IBM Watson with its advanced natural language capability to Linux clusters with the usual processors and software. One big challenge is getting access to detailed data on large enough patient populations—some big healthcare companies are investing a lot of money today to acquire more data. Another challenge is speed. An important decision-support goal over time is for the computer to spit out efficacy curves for treatment options in near-real time, while the patient is still sitting across from the doctor. Yet another challenge is the state of the data science—there’s a big need for tools that help users understand the data better, including benchmarks to verify that the results are useful.

HPCwire: How significant is the relative lack of HPC expertise and general computational literacy of most clinical physicians and even life scientists generally? The command line is hardly a friendly place for them. What, if anything, should be done to support them and to raise their computational skill level?

Conway: One of the biggest barriers across all of HPC is the C. P. Snow “two cultures” problem, where in the case of HPC you have computer scientists and domain scientists trying to communicate with each other using different languages. In precision medicine you might have HPC vendors talking about integer or floating point operations per second, while the buyers and users want to hear about cancer detections per second. My own opinion is that in precision medicine, to be successful HPC vendors will need to bend more toward the users than the other way around. I don’t think vendors can expect users to make a big effort to become more proficient in HPC. It will be interesting to hear what the panelists at SC’16 have to say about this.

HPCwire: How should we expect delivery of PM technology to evolve? IBM Watson has received a lot of attention using a cloud-like model while many institutions have on-premise resources. How will the PM delivery ecosystem (HPC infrastructure) evolve?

Conway: Again, you’ll get a fuller discussion of this during the SC panel session, but it seems clear that an effective precision medicine environment will involve both on-premise and cloud resources, presumably integrated in a way that’s transparent to users. You’ll need on-premise resources for brute force computing and cloud resources for things including data research, records transfer and general communication. Most healthcare systems already rely on private clouds for communication among providers and between providers and patients. The brute force computing will be needed for near-real time diagnosis and treatment planning.

HPCwire: What are the two or three examples of the most advanced HPC-based PM systems used today and what makes them distinct?

Conway: Let’s start with IBM Watson. In 2011, Watson stunned a huge American television audience by defeating two human past champions of the Jeopardy! game show in a competition match. The great achievement of this digital brain was its ability to “understand” natural language — specifically, natural language expressed in the interrogatory syntax of the game show. On the heels of this triumph, IBM announced in January 2014 that it would invest $1 billion to advance Watson’s decision-making abilities for major commercial markets, including healthcare. Not much later, in May 2015, IBM said 14 U.S. cancer treatment centers had signed on to receive personalized treatment plans selected by a Watson supercomputer. Watson has contracted since Jeopardy! days “from the size of a master bedroom to three stacked pizza boxes.” Watson will parse the DNA of each patient’s cancer and recommend what it considers the optimal medical treatment, so it’s a powerful decision-support tool for healthcare providers.

The Center for Pediatric Genomic Medicine at Children’s Mercy Hospital, Kansas City, Missouri, has been using supercomputer power to help save the lives of critically ill children. In 2010, the center’s work was named one of Time magazine’s top 10 medical breakthroughs. Roughly 4,100 genetic diseases affect humans, and these are the main causes of infant deaths. But identifying which genetic disease is affecting a critically ill child isn’t easy. For one infant suffering from liver failure, the center used 25 hours of supercomputer time to analyze 120 billion nucleotide sequences and narrowed the problem down to two genetic variants. This allowed the doctors to begin treatment with corticosteroids and immunoglobulin. Thanks to this highly accurate diagnosis of the problem and pinpointed treatment, the baby is alive and well today. For 48% of the cases the center works on today, supercomputer-powered genetic diagnosis points the way toward a more effective treatment.

Researchers at the University of Oslo (Norway) are using a supercomputer to help identify the genes that cause bowel and prostate cancer, two common forms of the disease. There are 4,000 new cases of bowel cancer in Norway every year. Only 6 out of 10 patients survive the first five years. Prostate cancer affects 5,000 Norwegians every year and 9 out of 10 patients survive. The researchers are employing the supercomputer to compare the genetic makeup of healthy cells and cancer cells, paying special attention to complex genes called fusion genes.

The Frédéric Joliot Hospital Department (Orsay, France) is using the powerful supercomputer at the French Alternative Energies and Atomic Energy Commission (CEA) in Bruyères-le-Châtel to improve understanding of how tracers used in PET scans for cancer diagnosis distribute themselves through the body. The goals of this research are to optimize PET scan data analysis and, later on, to personalize the PET scan process for each patient in order to produce better outcomes.

Doctors at Australia’s Victor Chang Cardiac Research Institute are using supercomputer-based gaming technology to identify how individuals’ genetic makeups can affect the severity of their heart rhythm diseases. The researchers built a virtual heart, then applied the recorded heartbeats of patients to the digital heart model in order to spot abnormal electrocardiogram signals. The whole process took 10 days using HPC, instead of the 21 years it would have taken with a contemporary personal computer. In other words, this important work would be impractical without the supercomputer.

HPCwire: To a large degree, mechanistic modeling and simulation – beyond compound structure analysis and docking scoring – hasn’t played a large role in the clinic or basic research. Do you think this will change and what will drive the change?

Anton 1 supercomputer specialized for life sciences modeling and simulation

Conway: Modeling and simulation will continue to play a key role in designing a wide array of medical technology products used in clinical practice, from heart pacemakers to diagnostic imaging tools such as MRI and PET scanners. M&S is also crucial for genome sequencing and precision dosing of pharmaceuticals, both of which are important for precision medicine. I think M&S and advanced analytics will go hand-in-hand in this emerging market.

HPCwire: What haven’t I asked that I should?

Conway: Just that precision medicine will be the next market segment IDC adds to the ones we track in our high performance data analysis, or HPDA, practice. Precision medicine will join fraud and anomaly detection, affinity marketing and business intelligence as new segments that are made up mainly of large commercial firms that have adopted HPC for the first time. We forecast that the whole HPDA server and storage market will exceed $5 billion in 2020. Of that amount, about $3.5 billion will come from existing HPC sites and about $1.6 billion will be added to the HPC market by new commercial buyers. Assuming that precision medicine fulfills its promise over the next decade, it is likely to become the single largest market for HPDA, that is, data-intensive computing using HPC resources.

Steve Conway, is research vice president in IDC’s High Performance Computing group where he plays a major role in directing and implementing HPC research related to the worldwide market for technical servers and supercomputers. He is a 25-year veteran of the HPC and IT industries. Before joining IDC, Conway was vice president of corporate communications and investor relations for Cray, and before that had stints at SGI and CompuServe Corporation.

]]>https://www.hpcwire.com/2016/11/04/idc-conway-sc16-precision-medicine-panel/feed/031373SC16 Showcases Use of HPC and Cloud In Cancer Researchhttps://www.hpcwire.com/2016/10/19/sc16-showcases-use-hpc-and-cloud-cancer-research/?utm_source=rss&utm_medium=rss&utm_campaign=sc16-showcases-use-hpc-and-cloud-cancer-research
https://www.hpcwire.com/2016/10/19/sc16-showcases-use-hpc-and-cloud-cancer-research/#respondWed, 19 Oct 2016 15:28:04 +0000https://www.hpcwire.com/?p=30937The effort to attack cancer with HPC resources has been growing for years. Indeed, it’s accurate to say the sequencing of the human genome was as much a tour de force of HPC as of the new DNA sequencers. Back in June, Department of Energy Secretary Ernst Moniz blogged on the effort (Supercomputers are key […]

]]>The effort to attack cancer with HPC resources has been growing for years. Indeed, it’s accurate to say the sequencing of the human genome was as much a tour de force of HPC as of the new DNA sequencers. Back in June, Department of Energy Secretary Ernst Moniz blogged on the effort (Supercomputers are key to the Cancer Moonshot) and in a few weeks the opening panel at SC16 is on Precision Medicine.

Part of the NCI effort involves changing the way diverse, geographically spread researchers work. Begun two years ago and recently extended for another year, NCI has three Cancer Genomics Cloud Pilots being centered at the Broad Institute, Institute for Systems Biology (ISB), and Seven Bridges Genomics. The cloud pilots work in conjunction with NCI’s Genomic Data Commons (GDC) initiative, which is a data sharing platform that promotes precision medicine in oncology. “It is not just a database or a tool; it is an expandable knowledge network supporting the import and standardization of genomic and clinical data from cancer research programs,” according to NCI.

The GDC contains NCI-generated data from some of the largest and most comprehensive cancer genomic datasets, including The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Therapies (TARGET). For the first time, these datasets have been harmonized using a common set of bioinformatics pipelines, so that the data can be directly compared.

Below is an excerpt from NCI’s description of the Cancer Genomics Cloud Pilots:

“The traditional model for analyzing genomic data involves individual researchers downloading data stored at a variety of locations, adding their own data, attempting to harmonize the data, and then computing over these data on local hardware. While this model has been successful for many years, it has become unsustainable given the enormous growth of biomedical data due to the prevalent use of next-generation sequencing technology in large scientific programs. The size of the data makes access and analysis difficult for anyone but the best-resourced institutions, in terms of both storage and computing capability…

“Key design principles for the CGC Pilots include: APIs for secure tool and data access, usability for biologists and clinicians as well as bioinformaticists and application developers, scalability, sustainability, extensibility to new data types without major refactoring, and open source, non-viral software licenses.”

All three CGC Pilots have chosen to implement their systems through commercial cloud providers – AWS and Google – and are collaborating on adopting common standards. Beyond these commonalities, the three project teams have distinct system designs, data presentation, and analysis resources to serve the cancer research community.”

Moniz’s June blogpost, though focused on supercomputers, captures the role of HPC in medical research: “Supercomputers are key to the Cancer Moonshot. These exceptionally high-powered machines have the potential to greatly accelerate the development of cancer therapies by finding patterns in massive datasets too large for human analysis. Supercomputers can help us better understand the complexity of cancer development, identify novel and effective treatments, and help elucidate patterns in vast and complex data sets that advance our understanding of cancer.”

]]>SUNNYVALE, Calif., Oct. 6 — Panasas, the leader in performance scale-out network-attached storage (NAS) today announced its collaboration with Western Digital (NASDAQ: WDC), to use iRODS (Integrated Rule-Oriented Data System) rules-based open source software to propel data discovery in life science research. The joint solution combines Panasas ActiveStor storage, the HGST Active Archive System and iRODS Data Management Software to create an easily managed, high-performance multi-tier storage infrastructure that accelerates and optimizes data access, simulations and data analysis for research organizations working to improve medical discoveries through bioinformatics. Details on the collaboration can be found in the HGST “Speed Research Discovery” use case document.

The rate of progress in life sciences research is accelerating exponentially, leading to important advances that generate – and rely upon – an ever-expanding mountain of valuable data. Key to accelerating life science application performance is implementing a high-performance data infrastructure that eliminates computing and storage bottlenecks, enables better collaboration and preserves simplicity so researchers can focus their efforts on discovery. The joint Panasas-HGST solution consists of the following components:

“Our solutions ultimately make data readily available for users, applications and analytics, helping to facilitate faster results and better decisions,” said Gary Lyng, senior director of marketing, Data Center Systems at Western Digital. “We are excited to be working with Panasas as the volume, velocity, variety and value of data generated by modern lab equipment along with varying application and workflow requirements make implementing the right solution all the more challenging – and we have the right solution.”

“We are excited to collaborate with Western Digital to deliver a storage solution that will help the world’s top researchers accelerate their discoveries,” said David Sallak, vice president, products and solutions at Panasas. “The data-intensive computing applications in life sciences require high performance with efficient, secure and cost-effective data management and together we are ready to meet those needs.”

Panasas is the performance scale-out NAS leader for unstructured data, driving industry and research innovation by accelerating workflows and simplifying data management. Panasas ActiveStor appliances leverage the patented PanFS storage operating system and DirectFlow protocol to deliver performance and reliability at scale from an appliance that is as easy to manage as it is fast to deploy. Panasas storage is optimized for the most demanding workloads in life sciences, manufacturing, media and entertainment, energy, government as well as education environments, and has been deployed in more than 50 countries worldwide. For more information, visit www.panasas.com.